ClusterClassify

ClusterClassify[data]

generates a ClassifierFunction[…] by partitioning data into clusters of similar elements.

ClusterClassify[data,n]

generates a ClassifierFunction[…] with at most n clusters.

Details and Options

  • ClusterClassify works for a variety of data types, including numerical, textual, and image, as well as dates and times and combinations of these.
  • The following options can be given:
  • CriterionFunctionAutomaticcriterion for selecting a method
    DistanceFunctionAutomaticthe distance function to use
    FeatureExtractorIdentityhow to extract features from which to learn
    FeatureNamesAutomaticfeature names to assign for input data
    FeatureTypesAutomaticfeature types to assume for input data
    MethodAutomaticwhat method to use
    MissingValueSynthesisAutomatichow to synthesize missing values
    PerformanceGoalAutomaticaspect of performance to optimize
    RandomSeeding1234what seeding of pseudorandom generators should be done internally
    WeightsAutomaticwhat weight to give to each example
  • By default, ClusterClassify will preprocess the data automatically unless a DistanceFunction is specified.
  • The setting for DistanceFunction can be any distance or dissimilarity function, or a function f defining a distance between two values.
  • Possible settings for PerformanceGoal include:
  • Automaticautomatic tradeoff among speed, accuracy, and memory
    "Memory"minimize the storage requirements of the classifier
    "Quality"maximize the accuracy of the classifier
    "Speed"maximize the speed of the classifier
    "TrainingSpeed"minimize the time spent producing the classifier
  • Possible settings for Method include:
  • Automaticautomatically select a method
    "Agglomerate"single linkage clustering algorithm
    "DBSCAN"density-based spatial clustering of applications with noise
    "NeighborhoodContraction"shift data points toward high-density regions
    "JarvisPatrick"Jarvis–Patrick clustering algorithm
    "KMeans"k-means clustering algorithm
    "MeanShift"mean-shift clustering algorithm
    "KMedoids"partitioning around medoids
    "SpanningTree"minimum spanning tree-based clustering algorithm
    "Spectral"spectral clustering algorithm
    "GaussianMixture"variational Gaussian mixture algorithm
  • The methods "KMeans" and "KMedoids" can only be used when the number of clusters is specified.
  • The following plots show results of common methods on toy datasets:
  • Possible settings for CriterionFunction include:
  • "StandardDeviation"root-mean-square standard deviation
    "RSquared"R-squared
    "Dunn"Dunn index
    "CalinskiHarabasz"Calinski–Harabasz index
    "DaviesBouldin"Davies–Bouldin index
    "Silhouette"Silhouette score
    Automaticinternal index
  • Possible settings for RandomSeeding include:
  • Automaticautomatically reseed every time the function is called
    Inheriteduse externally seeded random numbers
    seeduse an explicit integer or strings as a seed
  • ClusterClassify[…,FeatureExtractor"Minimal"] indicates that the internal preprocessing should be as simple as possible.

Examples

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Basic Examples  (3)

Train the ClassifierFunction on some numerical data:

Use the classifier function to classify a new unlabeled example:

Obtain classification probabilities for this example:

Classify multiple examples:

Plot the probabilities for the two different classes in the interval {-5,5}:

Train the ClassifierFunction on some colors by requiring the number of classes to be 5:

Train the ClassifierFunction on some unlabeled data:

Gather the elements by their class number:

Train the ClassifierFunction on some strings:

Gather the elements by their class number:

Scope  (11)

Classify real numbers:

Classify vectors:

Classify Boolean vectors:

Use the classifier to assign clusters to a new Boolean True, False vector:

Use the classifier to assign clusters to a Boolean 1, 0 vector:

Look at their probabilities:

Classify images:

Use the classifier to cluster new images:

Classify 3D images:

Classify colors:

Classify strings:

Use the classifier to cluster new strings:

Classify heterogeneous data:

Classify times:

Use the classifier to cluster the data:

Classify random reals:

Look at the classifier information:

Get a description for the specific method used:

Generate random points in the plane and visualize them:

Classify the data:

Classify new random points in the place:

Visualize the resulting clustering:

Classify the same test data using IndeterminateThreshold:

Visualize the resulting clustering including the Indeterminate cluster:

Options  (10)

CriterionFunction  (1)

Generate some separated data and visualize it:

Construct a classifier function using the Automatic CriterionFunction:

Construct a classifier function using the Calinski–Harabasz index as CriterionFunction:

Compare the two clusterings of the data:

FeatureExtractor  (1)

Create a ClassifierFunction from a list of images and classify new examples:

Create a custom FeatureExtractor to extract features:

FeatureNames  (1)

Generate a classifier function and give a name to each feature:

Use the association format to assign cluster to a new example:

The list format can still be used:

FeatureTypes  (1)

Generate a classifier function assuming numerical and nominal feature types:

Generate a classifier function assuming nominal feature types instead:

Compare the result on new examples:

Method  (2)

Generate some data using uniform distributions:

Classify the data:

Use Information to obtain a method description:

Look at the clustered data:

Classify the data using k-means:

Look at the clustered data:

Generate a large dataset using multinormal distributions and visualize it:

Use ClusterClassify to find clusters by specifying the method to use and look at the AbsoluteTiming:

Look at the resulting clustering:

Use ClusterClassify to find clusters without specifying the method to use and look at the AbsoluteTiming:

MissingValueSynthesis  (1)

Generate a large dataset using multinormal distributions and visualize it:

Use ClusterClassify to find clusters:

Get the top cluster probabilities for a point with missing data:

Set the missing value synthesis to replace each missing variable with its estimated most likely value given known values (which is the default behavior):

Replace missing variables with random samples conditioned on known values:

Get the distribution of likely clusters for the point by replacing missing variables repeatedly with the random sampling strategy:

PerformanceGoal  (1)

Generate a uniformly distributed dataset and visualize it:

Obtain a classifier from this data, with an emphasis on training speed:

Assign clusters to some randomly generated data and look at the AbsoluteTiming:

Obtain a classifier from this data, with an emphasis on the speed:

Assign clusters to some randomly generated data and look at the AbsoluteTiming compared to the one above:

Visualize the two clusterings for the test data and note how the setting "TrainingSpeed" gives better results:

RandomSeeding  (1)

Train several classifiers on random colors:

Compute the classifiers on a new color and observe that the result is always the same:

Train several classifiers on the same colors by using different values of the RandomSeeding option:

Compute the classifiers on and observe how the classifier differs:

Weights  (1)

Generate some separated data containing outliers:

Clusterize the data:

Use the classifier function to classify the outlier together with another point:

Clusterize the data, adding a big weight on the outlier:

Use the classifier function to classify the same points:

Applications  (3)

Train several classifiers on a small, uniformly distributed dataset:

Divide a triangle into segments by using the classifiers on a large number of uniformly distributed random points:

Generate some normally distributed data:

Clusterize the data without specifying the number of classes:

Clusterize the data, specifying the number of classes:

Find dominant colors in an image:

Cluster the data given by the array of pixel values of the image:

Use the classifier to assign clusters to each pixel:

Use the classifier function to find four dominant colors:

Use the classifier to get binary masks for each dominant color:

Wolfram Research (2016), ClusterClassify, Wolfram Language function, https://reference.wolfram.com/language/ref/ClusterClassify.html (updated 2020).

Text

Wolfram Research (2016), ClusterClassify, Wolfram Language function, https://reference.wolfram.com/language/ref/ClusterClassify.html (updated 2020).

BibTeX

@misc{reference.wolfram_2021_clusterclassify, author="Wolfram Research", title="{ClusterClassify}", year="2020", howpublished="\url{https://reference.wolfram.com/language/ref/ClusterClassify.html}", note=[Accessed: 22-October-2021 ]}

BibLaTeX

@online{reference.wolfram_2021_clusterclassify, organization={Wolfram Research}, title={ClusterClassify}, year={2020}, url={https://reference.wolfram.com/language/ref/ClusterClassify.html}, note=[Accessed: 22-October-2021 ]}

CMS

Wolfram Language. 2016. "ClusterClassify." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2020. https://reference.wolfram.com/language/ref/ClusterClassify.html.

APA

Wolfram Language. (2016). ClusterClassify. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/ClusterClassify.html